def measure(im, blur, noise=None, debug=False): focus_points = blur[0] #is_noisy = noise[2] size = cv.GetSize(im) npixels = size[0] * size[1] #if focused_regions is None: # focused_regions = image.new_from(im) # cv.Set(focused_regions, 0) # groups = form_groups(focus_points, # estimated_size=min(max(int(len(npixels) / 1000), 2), 15)) # #groups = form_groups(points, threshold=max(cv.GetSize(im))/16) # #print 'groups', len(groups) # draw_groups(groups, focused_regions) im2 = cv.CloneImage(im) g = Grid(cv.GetSize(im2)) if debug: image.show(g.draw(im2), "Image with Grid + ROI") roi = image.new_from(im, nChannels=1) cv.Set(roi, 0) #g.draw_lines(roi, thickness=int(max(min((size[0] + size[1]) * 1/100.0, 255), 1))) g.draw_regions(roi) area = cv.Sum(roi)[0] (_, face_rects), face_score = faces.measure(im) face_block = image.new_from(im, nChannels=1) cv.Set(face_block, 0) for r in face_rects: r.draw(face_block, color=cv.RGB(255,255,255), thickness=cv.CV_FILLED) if debug: face_roi = cv.CloneImage(im) cv.Set(face_roi, 0, image.invert(roi)) cv.Set(face_roi, 0, image.invert(image.threshold(face_block, threshold=1))) image.show(face_block, "Faces in Binary") image.show(g.draw(face_roi), "Face + ROI") return (im, ( measure_focused_roi(im, roi, area, focus_points, debug), #measure_color_roi(im, roi, area, focus_points, debug), measure_contrast(im, debug), measure_saturation(im, debug), faces.measure(im, debug)[1], ))
def draw(self, images, layers): if layers == 0: gray, r,g,b = images im = cv.CloneImage(r) for i in (g,b): im = image.add(im, i) white = image.new_from(gray) cv.Set(white, (0,0,0)) cv.Set(white, (255,255,255), image.invert(image.threshold(im, threshold=1))) im = image.Or(image.blend(im, gray), white) else: im = cv.CloneImage(images[layers-1]) if layers != 1: white = image.new_from(images[0]) cv.Set(white, (0,0,0)) cv.Set(white, (255,255,255), image.invert(image.threshold(im, threshold=1))) im = image.Or(im, white) return im
def draw(self, images, layers): if layers == 0: gray, r, g, b = images im = cv.CloneImage(r) for i in (g, b): im = image.add(im, i) white = image.new_from(gray) cv.Set(white, (0, 0, 0)) cv.Set(white, (255, 255, 255), image.invert(image.threshold(im, threshold=1))) im = image.Or(image.blend(im, gray), white) else: im = cv.CloneImage(images[layers - 1]) if layers != 1: white = image.new_from(images[0]) cv.Set(white, (0, 0, 0)) cv.Set(white, (255, 255, 255), image.invert(image.threshold(im, threshold=1))) im = image.Or(im, white) return im
def measure_color_roi(im, roi, area, focused_regions, debug=False): im = cv.CloneImage(im) g = Grid(cv.GetSize(im)) """ contours = Contours(image.threshold(focused_regions, threshold=1)).approx_poly() if debug: test = image.new_from(im) cv.Set(test, 0) for c in contours: i = 1 while c: cv.FillPoly(test, [[c[x] for x in range(len(c))]], cv.RGB(0,64*i,0)) c = c.v_next() i += 1 #contours.draw(test, levels=9) image.show(test, "Test") """ #mask = image.And(image.threshold(focused_regions, threshold=1), roi) # #canvas = image.new_from(im, nChannels=1) #cv.Set(canvas, 0) #if cv.CountNonZero(mask) <= 1: # return 0, 0 #contours = Contours(image.dilate(mask)).approx_poly() #for c in contours: # i = 1 # while c: # cv.FillPoly(canvas, [[c[x] for x in range(len(c))]], 255) # c = c.v_next() # i += 1 #mask = image.Or(mask, canvas) #if debug: # image.show(mask, "MASK") # #cv.Set(im, 0, image.invert(mask)) cv.Set(im, 0, image.invert(roi)) #area = cv.CountNonZero(image.threshold(im, threshold=1)) if debug: image.show(g.draw(im,thickness=2), "Image + ROI + Focus point mask") scores = [] im = image.rgb2gray(im) #canvas = image.And(plane, roi) quadrants = g.split_in_four(im) hist = [] for q,quad in enumerate(quadrants): #scores.append(cv.Sum(quad)[0] / float(area/4)) h = GrayscaleHist(value_range=(1,255)).use_image(quad) #image.show(h.to_img(), ['gray', 'red','green','blue'][i] + ' in ' + str(q)) hist.append(h.to_array()) scores = [] excluded_points = set([(2, 1), (3, 0)]) for i,h1 in enumerate(hist): for j,h2 in enumerate(hist): if i <= j or (i,j) in excluded_points: continue h = abs(h2-h1) ht = GrayscaleHist(value_range=(0,255)).use_array_as_hist(h) scores.append((h[5:].mean(), h[5:].std())) means = max([x[0] for x in scores]) stddevs = max([x[1] for x in scores]) return means/255.0, stddevs/255.0
for x in range(256): for y in range(size[1]): cv.Set2D(u, y, x, x) cv.Set2D(v, 255 - x, min(y, 255), x) image.show(u, "U") image.show(v, "V") rgb = image.luv2rgb(image.merge(l, u, v)) r, g, b = image.split(rgb) #xor = image.threshold(image.Xor(u,v), 0, cv.CV_THRESH_BINARY) xor = image.Xor(u, v) cv.Threshold(xor, xor, 16, 255, cv.CV_THRESH_TOZERO) image.show(rgb, "RGB") image.show(xor, "Xor") #cv.Sub(rgb, image.gray2rgb(image.invert(xor)), rgb) _, sat, _ = image.split(image.rgb2hsv(rgb)) image.show(sat, 'Saturation') #cv.Set(xor, 0, image.invert(image.threshold(sat, threshold=4))) cv.Sub(rgb, image.invert(image.gray2rgb(xor)), rgb) image.show(rgb, "Rgb - Xor") arr = image.cv2array(xor) avg_mean, avg_std = arr.mean(), arr.std() print cv.CountNonZero(xor) / float(size[0] * size[1]), avg_mean, avg_std cv.WaitKey() cv.DestroyAllWindows()
def measure(im, debug=False): size = cv.GetSize(im) npixels = size[0] * size[1] #print 'np', npixels focused = get_focus_points(im, debug) points = convert_to_points(focused) if debug: print "\t" + str( len(points)), '/', npixels, '=', len(points) / float(npixels) print "\tlen(points) =", len(points) image.show(focused, "4. Focused Points") saturation_score = 0 if not image.is_grayscale(im): edges = image.auto_edges(im) _, saturation, _ = image.split(image.rgb2hsv(im)) if debug: image.show(saturation, "5. Saturation") #saturation = image.laplace(image.gaussian(saturation, 3)) saturation = image.invert(saturation) mask = image.invert(image.threshold(im, threshold=16)) if debug: image.show(saturation, "5.3. Laplace of Saturation") cv.Set(saturation, 0, mask) cv.Set(saturation, 0, focused) if debug: image.show(mask, "5.6. Mask(focused AND invert(threshold(im, 16)))") image.show(saturation, "6. Set(<5.3>, 0, <5.6>)") saturation_score = cv.Sum(saturation)[0] / float(npixels * 255) print "\tSaturation Score:", saturation_score # light exposure h, s, v = image.split(image.rgb2hsv(im)) if debug: image.show(h, "7. Hue") image.show(s, "7. Saturation") image.show(v, "7. Value") diff = cv.CloneImage(v) cv.Set(diff, 0, image.threshold(s, threshold=16)) diff = image.dilate(diff, iterations=10) if debug: thres_s = image.threshold(s, threshold=16) image.show(thres_s, "8.3. Mask(threshold(<7.Saturation>, 16))") image.show(diff, "8.6. Dilate(Set(<7.Value>, 0, <8.3>), 10)") cdiff = cv.CountNonZero(diff) if cdiff > 0 and cdiff / float(npixels) > 0.01: test = cv.CloneImage(v) cv.Set(test, 0, image.invert(diff)) s = cv.Sum(test)[0] / float(cdiff * 255) if debug: print '\tLight Exposure Score:', s else: s = 0 if image.is_grayscale(im): return focused, (1, 1, 1, saturation_score, s) # we want to short circuit ASAP to avoid doing KMeans 50% of the image's pixels if len(points) > npixels / 2: return focused, (1, 1, 1, saturation_score, s) # we're so blurry we don't have any points! if len(points) < 1: return focused, (0, 0, 0, saturation_score, s) if debug: im2 = cv.CloneImage(im) focused_regions = image.new_from(im) cv.Set(focused_regions, 0) r = lambda x: random.randrange(1, x) groups = form_groups(points, estimated_size=min(max(int(len(points) / 1000), 2), 15)) #groups = form_groups(points, threshold=max(cv.GetSize(im))/16) #print 'groups', len(groups) hulls = draw_groups(groups, focused_regions) focused_regions = image.threshold(focused_regions, threshold=32, type=cv.CV_THRESH_TOZERO) min_area = npixels * 0.0005 densities = [h.points_per_pixel() for h in hulls if h.area() >= min_area] if debug: #image.show(focused, "Focused Points") image.show(focused_regions, "9. Focused Regions from <4>") cv.Sub( im2, image.gray2rgb( image.invert(image.threshold(focused_regions, threshold=1))), im2) image.show(im2, "10. threshold(<9>)") focused_regions = image.rgb2gray(focused_regions) densities = array(densities) c = cv.CountNonZero(focused_regions) c /= float(npixels) score = (c, densities.mean(), densities.std(), saturation_score, s) return focused, score
for x in range(256): for y in range(size[1]): cv.Set2D(u, y, x, x) cv.Set2D(v, 255-x, min(y, 255), x) image.show(u, "U") image.show(v, "V") rgb = image.luv2rgb(image.merge(l,u,v)) r,g,b = image.split(rgb) #xor = image.threshold(image.Xor(u,v), 0, cv.CV_THRESH_BINARY) xor = image.Xor(u,v) cv.Threshold(xor, xor, 16, 255, cv.CV_THRESH_TOZERO) image.show(rgb, "RGB") image.show(xor, "Xor") #cv.Sub(rgb, image.gray2rgb(image.invert(xor)), rgb) _, sat, _ = image.split(image.rgb2hsv(rgb)) image.show(sat, 'Saturation') #cv.Set(xor, 0, image.invert(image.threshold(sat, threshold=4))) cv.Sub(rgb, image.invert(image.gray2rgb(xor)), rgb) image.show(rgb, "Rgb - Xor") arr = image.cv2array(xor) avg_mean, avg_std = arr.mean(), arr.std() print cv.CountNonZero(xor) / float(size[0] * size[1]), avg_mean, avg_std cv.WaitKey() cv.DestroyAllWindows()
def measure(im, debug=False): size = cv.GetSize(im) npixels = size[0] * size[1] #print 'np', npixels focused = get_focus_points(im, debug) points = convert_to_points(focused) if debug: print "\t"+str(len(points)), '/', npixels, '=', len(points) / float(npixels) print "\tlen(points) =", len(points) image.show(focused, "4. Focused Points") saturation_score = 0 if not image.is_grayscale(im): edges = image.auto_edges(im) _, saturation, _ = image.split(image.rgb2hsv(im)) if debug: image.show(saturation, "5. Saturation") #saturation = image.laplace(image.gaussian(saturation, 3)) saturation = image.invert(saturation) mask = image.invert(image.threshold(im, threshold=16)) if debug: image.show(saturation, "5.3. Laplace of Saturation") cv.Set(saturation, 0, mask) cv.Set(saturation, 0, focused) if debug: image.show(mask, "5.6. Mask(focused AND invert(threshold(im, 16)))") image.show(saturation, "6. Set(<5.3>, 0, <5.6>)") saturation_score = cv.Sum(saturation)[0] / float(npixels * 255) print "\tSaturation Score:", saturation_score # light exposure h,s,v = image.split(image.rgb2hsv(im)) if debug: image.show(h, "7. Hue") image.show(s, "7. Saturation") image.show(v, "7. Value") diff = cv.CloneImage(v) cv.Set(diff, 0, image.threshold(s, threshold=16)) diff = image.dilate(diff, iterations=10) if debug: thres_s = image.threshold(s, threshold=16) image.show(thres_s, "8.3. Mask(threshold(<7.Saturation>, 16))") image.show(diff, "8.6. Dilate(Set(<7.Value>, 0, <8.3>), 10)") cdiff = cv.CountNonZero(diff) if cdiff > 0 and cdiff / float(npixels) > 0.01: test = cv.CloneImage(v) cv.Set(test, 0, image.invert(diff)) s = cv.Sum(test)[0] / float(cdiff * 255) if debug: print '\tLight Exposure Score:', s else: s = 0 if image.is_grayscale(im): return focused, (1, 1, 1, saturation_score, s) # we want to short circuit ASAP to avoid doing KMeans 50% of the image's pixels if len(points) > npixels/2: return focused, (1, 1, 1, saturation_score, s) # we're so blurry we don't have any points! if len(points) < 1: return focused, (0, 0, 0, saturation_score, s) if debug: im2 = cv.CloneImage(im) focused_regions = image.new_from(im) cv.Set(focused_regions, 0) r = lambda x: random.randrange(1, x) groups = form_groups(points, estimated_size=min(max(int(len(points) / 1000), 2), 15)) #groups = form_groups(points, threshold=max(cv.GetSize(im))/16) #print 'groups', len(groups) hulls = draw_groups(groups, focused_regions) focused_regions = image.threshold(focused_regions, threshold=32, type=cv.CV_THRESH_TOZERO) min_area = npixels * 0.0005 densities = [h.points_per_pixel() for h in hulls if h.area() >= min_area] if debug: #image.show(focused, "Focused Points") image.show(focused_regions, "9. Focused Regions from <4>") cv.Sub(im2, image.gray2rgb(image.invert(image.threshold(focused_regions, threshold=1))), im2) image.show(im2, "10. threshold(<9>)") focused_regions = image.rgb2gray(focused_regions) densities = array(densities) c = cv.CountNonZero(focused_regions) c /= float(npixels) score = (c, densities.mean(), densities.std(), saturation_score, s) return focused, score